Verifying The Accuracy Of Machine Vision Algorithms And Systems

Abstract

The Purpose of this paper is threefold: (a) to summarize important parameters and procedures for verifying the measurement and recognition (classification) accuracy of machine vision algorithins/systems; (b) to alert the machine vision research community to the current, very inadequate practice in this important area; and (c) to propose some measures to improve this situation. Two example applications from our practice are given in order to illustrate experimental verification procedures we presented. The motivation for the paper is based on the fact that machine vision systems are very hard to model or simulate accurately, so realistic large scale experiments seem to be the only reliable means of assessing their accuracy. However, in the machine vision (research) community this veffication is seldomly done ade- quately. We feel that until this situation is improved, the transfer of research ideas into practice will be difficult.